Current Projects

Current Projects

Exploring innovative biotechnology solutions for sustainable protein production and food innovation

Lactoferrin Project

Cow-free Lactoferrin Production

This collaborative research project brings together teams from multiple universities to evaluate a novel bioengineered protein: precision fermentation–derived lactoferrin.

Lactoferrin is a multifunctional glycoprotein known for its antimicrobial, anti-inflammatory, and immunomodulatory properties. Traditionally sourced from bovine or human milk, our team aims to replicate its bioactivity through precision fermentation using engineered yeast strains.
Research Goals: Assess how our recombinant lactoferrin compares to natural counterparts during digestion, focusing on peptide release patterns, preservation of glycosylation sites, and biochemical differences after digestion.
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Smart Instruments Project

Smart Instruments for Precision Fermentation

Revolutionizing protein production through AI-powered automation and sustainable biotechnology.

By combining cutting-edge artificial intelligence with advanced industrial manufacturing, our team designs and builds purpose-driven instruments tailored for precision fermentation workflows. These tools automate labor-intensive steps in microbial screening and protein development.
Key Innovation: Our in-house Microbial Colony Picker utilizes state-of-the-art computer vision, providing both compact (8-channel) and high-throughput (24-channel) models for precise and rapid colony identification and isolation.
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Sweet Proteins Project

Sweet Proteins and Thermostability

Revolutionizing protein engineering through AI-driven design of heat-stable sweet proteins for sustainable food applications.

Sweet proteins offer a revolutionary approach to reducing sugar consumption while maintaining the sweet taste that consumers crave. However, they must remain stable under heat during food processing to preserve their function.
AI-Driven Solutions: Leveraging evolutionary sequence data and advanced AI models to accelerate the context-specific design of thermostable proteins, including sweet proteins, without relying on supervised training or exhaustive datasets.
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